Learning Unknown Service Rates in Queues: A Multiarmed Bandit Approach

DOI10.1287/opre.2020.1995zbMath1466.90027arXiv1604.06377OpenAlexW3086913918MaRDI QIDQ4994160

Subhashini Krishnasamy, Sanjay Shakkottai, Ramesh Johari, Rajat Sen

Publication date: 17 June 2021

Published in: Operations Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1604.06377

zbMATH Keywords

schedulingqueueing systemsregretbandit algorithms

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Queues and service in operations research (90B22)

Cites Work

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• Batched bandit problems
• Admission and routing of soft real-time jobs to multiclusters: design and comparison of index policies
• Combinatorial bandits
• Regret bounds for restless Markov bandits
• Marginal productivity index policies for scheduling a multiclass delay-/loss-sensitive queue
• Dynamic priority allocation via restless bandit marginal productivity indices
• Exploration-exploitation tradeoff using variance estimates in multi-armed bandits
• Asymptotically efficient adaptive allocation rules
• Dynamic scheduling with convex delay costs: The generalized \(c\mu\) rule
• Scheduling of multi-class multi-server queueing systems with abandonments
• ON THE OPTIMALITY OF AN INDEX RULE IN MULTICHANNEL ALLOCATION FOR SINGLE-HOP MOBILE NETWORKS WITH MULTIPLE SERVICE CLASSES
• Multi‐Armed Bandit Allocation Indices
• Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis
• The cμ rule revisited
• Heavy Traffic Limit Theorems for Queues: A Survey
• Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
• Finite-time analysis of the multiarmed bandit problem